Deep Learning 썸네일형 리스트형 Improve noise estimation performance 🔹 1. Denoising with Confidence Estimation (Failed)The confidence might be inherently learned during the noise estimationTrain the noise estimator to also output a confidence map.Use this to weight how much noise to subtract:denoised = bev_feat - confidence * noiseHelps avoid over-subtraction in uncertain regions.🔹 2. Feature Consistency Loss : Alrady being appliedAdd a loss between clean BEV f.. 더보기 Project lidar point cloud into a camera image in nuScenes import osfrom NuscenesDataset.nuscenes import NuScenesfrom PIL import Imagefrom nuscenes.utils.data_classes import LidarPointCloudfrom pyquaternion import Quaternionimport numpy as npimport matplotlib.pyplot as pltnusc = NuScenes(version='v1.0-trainval', dataroot="/home/dooseop/DATASET/nuscenes/", verbose=False)my_sample = nusc.sample[10]# my_sample = nusc.sample[10]# nusc.render_pointcloud_in_i.. 더보기 fatal factors that make the self-supervised training for monodepth estimation extremely difficult when trained on nuScenes Human: I've been working on monodepth estimation in a self-supervised manner and have referred to monodepth2. While training on nuScene, I found it hard to train the model for some reason. What would be fatal factors for the sucessful training? ChatGPT: Training a self-supervised monodepth model like Monodepth2 on a complex dataset like nuScenes can be tricky. Here are some fatal factors that co.. 더보기 Noise estimation idea 2 Human: Assume we want to OGMs for 2 classes, which results in {X_1, X_2}. When estimating the intrinsic noise in X1, I think I can take advantage of X_2 as a prior information. What would be a common practice? ChatGPT: Leveraging X2X_2X2 as Prior Information for Noise Estimation in X1X_1X1Since you are estimating the intrinsic noise in X1X_1X1, it makes sense to use X2X_2X2 as prior informat.. 더보기 Improving DDPD noise estimation ChatGPT: To improve the performance of your U-Net-based intrinsic noise estimation network, consider the following architectural modifications: 1. Enhancing Feature Representationsa. Using Residual Connections (ResUNet)Replace standard convolutional blocks with Residual Blocks to ease gradient flow and improve training stability.Residual blocks reduce vanishing gradient issues, helping the netwo.. 더보기 Class-specific DDPM In **Denoising Diffusion Probabilistic Models (DDPMs)**, conditioning the input with class-specific information is a common practice to guide the model in generating samples from a specific category. The most widely used techniques for class conditioning include:### **1. Class Embedding via Concatenation** - The class label is first converted into an embedding vector (e.g., using an **embeddin.. 더보기 [Pytorch] Inputting specified model parameters to optimizer params_to_optimize = [param for name, param in self.model.named_parameters() if self.check_name_validity(TRAIN_WEIGHT_LIST, name)]self.opt = optim.AdamW(params_to_optimize, lr=config['lr'], weight_decay=config['w_decay']) 더보기 How to count the number of parameters of a NN and measure FLOPs required for the NN from fvcore.nn import FlopCountAnalysisdef count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) ResNet = ConvNet(use_pretrained=True, feature_extract=False, resent_model=saved_args.resnet_model)N_param = count_parameters(ResNet) / 1e6input_tensor = torch.randn(1, 3, 320, 640)flops = FlopCountAnalysis(ResNet, input_tensor.. 더보기 Denoising Diffusion Probabilistic model (1) https://towardsdatascience.com/diffusion-model-from-scratch-in-pytorch-ddpm-9d9760528946 Diffusion Model from Scratch in PytorchImplementation of Denoising Diffusion Probabilistic Models (DDPM)towardsdatascience.com (2) https://jang-inspiration.com/ddpm-1https://jang-inspiration.com/ddpm-2 [논문리뷰] DDPM: Denoising Diffusion Probabilistic ModelDDPM의 Loss Function과 각 term들의 의미, 그리고 Sampling 및 T.. 더보기 Initialize specific parameters of a NN with pre-trained ones and stop them from learning # pre-trained & target state dictfile_name = './saved_models/nuscenes_CVT_model1100/saved_chk_point_27.pt'pre_state_dict = torch.load(file_name, map_location=torch.device('cpu'))['model_state_dict']target_state_dict = model.state_dict()# copy from pre-trained to targetfor name, param in pre_state_dict.items(): if 'shallow' in name: if name.replace("module.", "") in target_state_dict: .. 더보기 이전 1 2 3 4 ··· 7 다음 목록 더보기